214 results about "Noise floor" patented technology

The invention discloses a periodogram and incoming wave direction finding fused multi-unmanned aerial vehicle signal identification method and system, and relates to a signal processing technology, and the method comprises the steps: obtaining to-be-identified signals with time domain aliasing and frequency domain non-aliasing; suppressing the noise of the to-be-identified signal by using an average periodogram method and smooth filtering, and calculating a bottom noise value; according to the calculated bottom noise value, setting a judgment threshold to detect a target unmanned aerial vehicle signal; estimating the start-stop frequency and bandwidth of the detected target unmanned aerial vehicle signal; performing signal separation by using an FIR filter according to the estimated starting and ending frequency and bandwidth; and executing incoming wave lateral direction based on the separated target unmanned aerial vehicle signal so as to complete signal identification. According to the method, unmanned aerial vehicle signal detection is completed through an average periodogram method, unmanned aerial vehicle weak signals which are sparsely distributed in space are further obtained through a direction finding method, and weak signal unmanned aerial vehicles which are far in distance are detected.

A reference system is provided for generating a clock signal with a tunable noise pedestal for driving a signal generator. The reference system includes a reference source configured to generate a reference signal and a multiplier chain, including a noise pedestal generator. The noise pedestal generator includes a noise pedestal attenuator configured to attenuate the reference signal to degrade a noise floor of the reference signal, a VGA configured to adjust the attenuated reference signal to a desired signal level, a bandpass filter configured to filter out excess noise from the adjusted reference signal to form a noise pedestal, and a switch arrangement configured to selectively input the reference signal to a first path including the noise pedestal attenuator and the VGA, and a second path bypassing the noise pedestal attenuator and the VGA. The clock signal includes the noise pedestal when the first path is selected.

The invention relates to an adaptive noise floor energy detection method and system based on FFT frequency spectrum accumulation, and the method comprises the steps: carrying out the FFT frequency spectrum accumulation of an electromagnetic signal sampled by an ADC in an FPGA, and carrying out the channel division of a broadband frequency spectrum into sub-bands; counting and estimating the top and the bottom of the noise by sub-bands one by one; carrying out median filtering processing, subtraction and modular operation according to the obtained data to obtain optimal values of the energy bottom and the noise energy fluctuation range of each channel, carrying out summation to obtain a noise floor of each sub-band, and carrying out median filtering once to obtain a final noise floor value of each sub-band; and a noise factor is introduced to obtain a final adaptive constant false alarm threshold, and adaptive detection of noise floor energy is realized through the threshold. According to the invention, a parallel operation architecture based on the FPGA is adopted, so that the method is simple in calculation and high in sensitivity, and has the capability of detecting each sub-band signal at the same time.

The invention discloses a multi-bandwidth spectrum data signal adaptive extraction method based on a signal-to-noise ratio threshold, and relates to the technical field of spectrum signal extraction. The frequency spectrum data collection covers 30MHz-6GHz, the sampling rate is 20MHz, 16-bit quantization is realized, and the hardware fluctuation is eliminated through the dual-channel design; according to noise floor estimation, 50 frames of no-signal samples are taken, mean value calculation is carried out through a sliding window, then median filtering is carried out, and the noise power and confidence interval of each frequency point are obtained; basic values are set for a signal-to-noise ratio threshold according to frequency bands, dynamic adjustment is performed in combination with real-time noise standard deviation, and compensation is added to a known frequency band; for multi-bandwidth detection, wavelet packet decomposition is adapted to 100kHz-5MHz, candidate signals are marked when energy detection exceeds a threshold, and a range is determined by morphological filtering; extracting parameters such as center frequency, analyzing time-frequency characteristics through short-time Fourier transform, and calculating 4-order cumulants to establish feature vectors; coherent accumulation times are adjusted according to a signal-to-noise ratio, energy is superposed through phase alignment, and a purified signal is output through band-pass filtering. According to the invention, the signal detection precision and adaptability in a complex environment are improved, and misjudgment is reduced.

The invention discloses a self-adaptive large dynamic detection noise floor suppression system and a control method thereof. The system comprises a filtering selection front-end module, a detection synthesis module, a frequency measurement receiving module and an FPGA feedback control module. The method is composed of four main parts, namely a filtering selection front end, detection synthesis, frequency measurement receiving and FPGA feedback control. Wherein the filtering selection front end and the frequency measurement receiving end receive two groups of external radio frequency signals at the same time. And frequency measurement is carried out through a mature single-bit receiving scheme in frequency measurement receiving, and a result is sent to the feedback control module, so that switching of a filtering channel and suppression of a noise floor are realized. According to the invention, the broadband large dynamic DLVA detection sensitivity is obviously improved.

The invention provides a data processing and output circuit and method for a high-energy particle detection pixel sensor chip, and relates to the technical field of high-energy particle detection, and the method comprises the steps that each data processing unit receives ADC output of multiple channels, and carries out the validity discrimination of the received original data of each channel in a polling mode; effective case information in each data processing unit is summarized through a data integration circuit, and the effective case information is cached into a sparse signal output buffer; the data output interface circuit receives the information in the sparse signal output buffer, judges whether effective information needing to be output exists or not, and reads formatted data if the effective information exists. According to the invention, the output signal of the quantization circuit is discriminated so as to distinguish an effective case and a noise background, only pixel data containing an effective particle hit event is subjected to position correlation and coding, pixels not containing the effective hit event are skipped, and whether the data output interface outputs data to the outside is determined according to whether the effective case exists or not.

The invention provides a radar signal adaptive threshold generation method, which comprises the following steps of: when a threshold is generated, assigning a lower limit of a received system binding threshold as a current threshold value; opening a time window with the point number of M, and counting the time window with the point number of M: counting every M points as a counting period, and recording instantaneous amplitude values of a plurality of points in the time window in one counting period; if the instantaneous amplitude values of the majority of points in the time window do not exceed the current threshold, increasing the current threshold on the basis of the current threshold; if the instantaneous amplitude values of the majority of points in the time window exceed the current threshold and 1.25 times of the average level of the noise floor exceeds the current threshold, the current threshold is increased on the basis of the current threshold; and when 1.25 times of the average level of the noise floor is lower than the current threshold, the current threshold is lowered. The invention further provides a computer readable storage medium which is used for realizing the radar signal adaptive threshold generation method.

A system that performs beam selection and beam merging using beam-specific signal quality metrics corresponding to a minimum noise floor. For example, a device may track a minimum noise floor for each beam, determine a highest minimum noise floor across the beams, and determine a noise floor ratio between the beam-specific minimum noise floor and the highest minimum noise floor. Using a combination of the noise floor ratio and signal-to-noise ratio (SNR) values, the device may perform beam selection by prioritizing low background noise as well as high SNR to select a pre-defined beam group. In addition, the device may use the noise floor ratio to perform beam merging and generate single-channel output audio data using the selected beam group. For example, the device may scale the beams based on a combination of the SNR value and the noise floor ratio.

The invention relates to the technical field of low-power-consumption electronics, and discloses a low-power-consumption instantaneous frequency measurement system which comprises a radio-frequency signal acquisition module, a signal time-frequency conversion module, a noise floor coding module and the like. The system generates a feature tensor through time-frequency analysis of a radio frequency signal, realizes dynamic threshold fusion in combination with noise floor sparse coding, extracts a multi-scale transient frequency feature by using a pyramid convolutional neural network, dynamically suppresses noise based on a signal-to-noise ratio weight, and reconstructs a real-time spectrum distribution image. The power consumption control module can adjust the ADC sampling rate and the clock frequency of the processing unit according to the signal strength, and the power management unit adopts multi-voltage domain independent power supply. The system solves the problems of frequency measurement precision and power consumption under complex noise, is suitable for the fields of wireless communication and the like, and has the characteristics of high precision, low power consumption, high spectral resolution, visualization and intuition.

A method of determining a location of a mobile device in the presence of a spoofing signal includes receiving a GNSS signal and detecting a spoofing condition based on a signal strength of the GNSS signal. The spoofing condition may comprise an increase in the signal strength, the signal strength exceeding a threshold value, an increase in a noise floor related to a measurement of the signal strength, or a combination thereof. The method may further comprise, responsive to detecting the spoofing condition, providing an indication of the GNSS signal as a spoofing signal.

The invention relates to the technical field of optical fiber sensing signal processing, in particular to a DAS signal floor noise elimination method, system and equipment based on statistical analysis, and the method comprises the steps: initializing system parameters, and loading a DAS application field data matrix; performing slicing and frequency domain transformation on the DAS original data; dynamically determining a peak threshold value, and performing adaptive peak searching according to the peak threshold value; sorting the identified and screened local maximum values in a descending order, and selecting frequencies corresponding to the local maximum values as main frequency candidates; performing clustering analysis on the selected dominant frequency candidates; calculating the cumulative proportion of each main frequency in the total statistics, selecting a center frequency, and forming the multi-frequency band elimination of the monitoring unit by using band elimination frequency bands formed by different center frequencies; and adding the multi-frequency band elimination to a statistical filtering frequency band set of all the point locations, storing the statistical filtering frequency band set of all the point locations as a statistical model, and performing background noise elimination on the DAS original signal based on the statistical model.

The invention discloses a vibration sensor background noise testing method based on a vacuum cavity, and the method comprises the steps: constructing an active-passive composite vibration isolation platform in a sealed vacuum cavity, rigidly fixing a to-be-tested sensor on the vibration isolation platform in a testing process, and guaranteeing that a sensitive axis of the to-be-tested sensor is orthogonal to a cavity reference; and an output signal is recorded for a long time at a sampling rate greater than or equal to 256Hz through a high-precision acquisition system. The data processing comprises polynomial detrending, sliding window de-averaging, multi-band elimination filtering and power spectral density (PSD) analysis so as to extract the equivalent amplitude and frequency domain characteristics of the background noise. In order to ensure the credibility of the result, the test needs to be repeated for three times, the consistency of the PSD curve is compared, and the deviation does not exceed 5%. The method combines the vacuum environment, composite vibration isolation, signal preprocessing and spectrum analysis technologies, can accurately quantify the background noise characteristics of the sensor, and is suitable for research and development verification and performance evaluation of the high-sensitivity sensor.

The invention discloses an optimal linear point searching method for a silicon-based Mach-Zehnder modulator. According to the method, the linearity of a corresponding working point is estimated according to the power of a fundamental component and a third-order intermodulation component of an output signal of the modulator, and the optimal linear point of the modulator can be obtained based on the maximum estimated value of the linearity. According to the method, a maximum entropy spectral subtraction method is adopted to reduce a signal noise floor, and a violent search method is adopted to retrieve an optimal combination of a modulator upper and lower arm phase difference and a reverse bias voltage to obtain an optimal working point. Compared with a conventional linearity test method that the linearity is obtained by testing when the working point of the modulator is set to be an orthogonal point, the optimal linear point searched by the method can obviously improve the linearity of the modulator, and a high-linearity silicon-based integrated microwave photon system can be realized.

The invention discloses a network signal interference detection system based on spectrum analysis. The system comprises a network signal acquisition module, a spectrum preprocessing module, a spectrum calculation module, a frequency separation spectrum kurtosis analysis module, a Bayesian online change point detection module, a parameter linkage module, an interference discrimination module and a result output module. The frequency separation spectrum kurtosis analysis module outputs kurtosis statistics and noise bottom estimation according to time slices based on the power spectrum; the Bayesian online change point detection module updates and outputs the change point position, the confidence coefficient and the adaptive time window parameter online; the parameter linkage module updates the frequency separation spectrum kurtosis analysis module according to the data; and the interference judgment module generates an interference event list, and the result output module outputs alarm and troubleshooting positioning data. According to the invention, automatic detection and discrimination of same-frequency superposition, adjacent-frequency superposition, burst intermittence and noise bottom uplift interference are realized, the accuracy is improved, and the troubleshooting cost is reduced.

The present invention discloses a base station construction plan guidance method, device and storage medium, the method comprising: determining a first test result that meets a first requirement by adjusting the isolation distance between a first node and a second node; the first requirement comprising: a noise floor rise value of the second node being a first target value; the first test result comprising: a first isolation distance between the first node and the second node; determining a second test result that meets a second requirement by adjusting the transmission power of the first node; the second requirement comprising: a noise floor rise value of the second node being a second target value; the second test result comprising: the transmission power of the first node; and determining a target isolation distance and / or a target noise floor rise value under a target rate requirement based on the first test result and the second test result.

The invention discloses a broadband random noise source with a high excess noise ratio, and belongs to the technical field of microwave and millimeter wave vacuum devices. The broadband random noise source comprises a medium-loaded gyrotron traveling wave tube and a Bragg filter, a wide-spectrum noise floor signal is generated by increasing the axial length of a medium loading circuit in the medium loading gyrotron traveling wave tube, and reflection filtering is performed on the wide-spectrum noise floor signal by arranging a Bragg filter between a collecting stage and an output window, so that a high-flatness wide-spectrum random noise signal is obtained. The characteristics of high power, high frequency and wide bandwidth of the gyrotron traveling wave tube for amplifying microwaves are utilized, noise floor signals can be amplified to generate high-power noise signals, meanwhile, the Bragg filter is loaded to carry out filtering and power balancing on the noise floor signals, and therefore high-power, wide-frequency-spectrum and high-flatness random noise signal output is achieved. And the output power of the generated noise signal is effectively improved.

This application discloses a wind tunnel-based noise detection method, apparatus, storage medium, and electronic device. The method includes: when testing a test piece in a wind tunnel, acquiring a measured noise spectrum in the testing environment and obtaining the operating frequency of the wind tunnel's inverter; matching the background noise spectrum corresponding to the operating frequency in a background noise spectrum library based on the operating frequency; aligning and dividing the measured noise spectrum and the background noise spectrum using a frequency band division rule adapted to the inverter's operating frequency to obtain multiple frequency band units; based on the acoustic energy density of the measured noise spectrum and the background noise spectrum within each frequency band unit, extracting the energy component corresponding to the background noise spectrum from the measured noise spectrum through directional orthogonal projection, and reconstructing the spectrum of the residual energy after extraction to obtain the effective aerodynamic noise spectrum of the test piece; and calculating the noise detection result of the test piece based on the effective aerodynamic noise spectrum.

A wireless device is configured to transmit radar signals at extremely low spectral density so that the receiver can be operated at the same time without saturating and thereby enabling full-duplex operation. To achieve sufficient range, beamforming is used together with a very long correlation time for detection of the radar signal, in which case the receiver can resolve backscattered or reflected radar signals far below the noise floor, while the bandwidth of the transmitted radar signal can be very high. The wide bandwidth enables the wireless device to maintain a low power spectral density while increasing output power, and also increase the depth resolution. The extremely low power spectral density even in the transmission beam direction makes the probability that communication will be disturbed very low. The high bandwidth calls for digital filters for both transmitted and received signals to equalize the radar frequency response characteristics of the radio unit.

Systems and methods for a MEMS microphone package are disclosed. The MEMS microphone package may include a first port to direct sound to a MEMS system, including a die substrate, an acoustic membrane, and one or more plates. The MEMS microphone package may include an ASIC to produce microphone output based on an electrical signal, a PCB, a lid, and a second port. The first port and the second port may define a front volume and a back volume respectively. The second port may increase the back volume to improve sensitivity and reduce the acoustic sensor's noise floor to improve signal-to-noise ratio.

[Problem] To provide a determination device, a determination method, a determination program, and a radar device for determining the presence or absence of radio wave interference that does not cause a noise floor rise even when there is radio wave interference and does not require a special pattern provided for radio wave interference. [Solution] A determination device according to one embodiment of the present technology is provided with a reception unit and a determination processing unit. The receiving unit receives a first reflected chirp signal generated by a first transmission chirp signal transmitted toward an object by a transmitting device and reflected from the object, and a second reflected chirp signal generated by a second transmission chirp signal transmitted by the transmitting device at a time different from a time at which the first transmission chirp signal is transmitted and reflected from the object. A determination processing unit compares a plurality of arrival angle spectrums generated based on a plurality of corrected signals obtained by performing a plurality of velocity aliasing correction processes on the first reflected chirp signal and the second reflected chirp signal, and determines whether or not a useless signal is received.

This invention discloses a magnetotelluric denoising method and system based on a physics-driven and conditional diffusion model, belonging to the field of electromagnetic signal processing technology. This method integrates the advantages of neural networks and the physical characteristics of magnetotelluric signals, and proposes a physics-driven synthesis strategy based on statistical priors of measured source data and a curriculum-based dynamic physics-driven noise addition strategy based on training progress factors. Addressing the scarcity of high-quality, clean samples, a clean background field that highly approximates real physical characteristics is synthesized using a parametric mathematical model. The introduction of curriculum-based dynamic physics-driven noise addition (such as noise floor annealing, square wave incrementing, and morphological clustering pulses) accurately simulates natural background degradation and complex human interference, enabling the model to adapt to extremely complex time-frequency characteristics under different geological structural backgrounds. This completely overcomes the domain shift problem caused by static Gaussian white noise training, constructs a richer sample library, and lays the foundation for accurate denoising of conditional diffusion models.

The present disclosure relates to an audio processing method and device, electronic equipment and storage medium, the method comprising: converting an audio signal of a target audio device collected to a frequency domain to obtain first frequency domain information of the audio signal; wherein the first frequency domain information comprises amplitude values of different frequency components of the audio signal; determining a reference value according to the amplitude values; determining noise floor information of the audio signal according to differences between the amplitude values of different frequency components and the reference value; and the noise floor information is used to reflect a noise floor processing capability of the target audio device.

This application discloses a method and product for self-diagnosing external interference. The method includes: acquiring the inherent parameters of the frequency-using terminal to calculate the theoretical noise floor; measuring the total received power in real time and calculating the super-noise power accordingly; when the super-noise power exceeds a preset power threshold, acquiring the baseband demodulation quality parameters of the current channel; and performing correlation discrimination based on the super-noise power and baseband demodulation quality parameters. When the super-noise power is high and the demodulation quality is below a preset threshold, it is determined to be external interference; otherwise, it is determined to be a strong useful signal. This application establishes a two-dimensional causal correlation model between physical layer power and baseband layer quality within the terminal, enabling real-time and objective self-diagnosis of external interference from the terminal's own perspective, thus improving the objectivity, timeliness, and accuracy of the diagnosis.

The present invention relates to a method and device for estimating channel matching response noise reduction of a UWB receiver, wherein the method comprises: obtaining a coherent cumulative output signal; obtaining an initial noise floor power for the coherent cumulative output signal, and obtaining a noise threshold based on the initial noise floor power; extracting a noise signal in the coherent cumulative output signal according to the noise threshold, and obtaining a noise floor power for the extracted noise signal in the coherent cumulative output signal, and obtaining a new noise threshold based on the noise floor power; judging whether the number of iterations has been reached, if the number of iterations has been reached, proceeding to the next step, otherwise returning to the previous step; marking the part of the coherent cumulative output signal that exceeds the noise threshold as a useful signal; and setting the unmarked signal in the coherent cumulative output signal to zero. The present invention can accurately approximate the noise variance estimation.

The invention relates to the field of noise elimination, in particular to a method, a medium and equipment for eliminating FSK noise in a cockpit voice record. Comprising the following steps: determining whether an FSK noise section and a reference audio section on a to-be-processed recorded audio are background noise or not; if the FSK noise section and the reference audio section are both non-background noise, the frequency domain similarity between the first discriminant audio and the first reference audio is greater than a first similarity threshold, and the time domain similarity between the first discriminant audio and the first reference audio is greater than a second similarity threshold, determining that the first discriminant audio and the first reference audio are not background noise; if so, the audio of the FSK noise segment is replaced with the audio in the reference audio segment. According to the method, the characteristics of CVR multi-channel recording are utilized, the audio similarity of two microphone channels is compared, noise is eliminated in an automatic mode, the method is suitable for rapidly processing single or multiple records, and the cabin sound analysis efficiency is improved.

Background noise estimation and noise reduction are disclosed, in an embodiment, a method includes: obtaining an audio signal; dividing the audio signal into a plurality of buffers; determining time-frequency samples of each buffer of the audio signal; determining, for each buffer and each frequency, a median (or mean) of energies and a measure of energy variation based on samples in the buffer and samples in adjacent buffers, the samples in the buffer and the samples in the adjacent buffers together spanning a specified time range of the audio signal; combining the median (or mean) of energies and the measure of energy variation into a cost function; for each frequency: determining a signal energy of a particular buffer of the audio signal corresponding to a minimum of the cost function; selecting the signal energy as an estimated background noise of the audio signal; and using the estimated background noise to reduce noise in the audio signal.

The invention discloses an optical fiber array coupling system and method based on multi-source data fusion, and relates to the technical field of optical fiber array coupling system.When an automatic coupling machine receives a working instruction, the system firstly starts a pre-coupling process of an optical fiber array, fuses and analyzes multi-source vibration state data, corrects operating parameters in real time, effectively suppresses initial mechanical artifacts, and finally, the optical fiber array is subjected to multi-source data fusion analysis; the adaptability of equipment to external disturbance and signal ground noise is improved, after initialization of operation parameters is completed, the system enters an actual coupling process, key working condition parameters are continuously monitored, and whether further optimization and adjustment are needed or not is dynamically judged, so that the mechanical vibration amplitude and response dispersion in the coupling process are reduced, and the coupling efficiency is improved. According to the optical fiber array coupling detection system, signal bottom noise and mechanical artifact superposition amplification are prevented, the signal quality and the structural stability are improved, when it is detected that an optical fiber array completes coupling, the system executes locking and steady-state processing according to a preset sequence, and the connection anti-interference performance and the long-term stability are improved through technological solidification and closed-loop parameter adjustment.

The present invention provides a method for determining the background noise of a low-noise MHD angular velocity sensor and a manufacturing method thereof. The background noise determination method comprises: determining the parameter values ​​of multiple equivalent components included in the equivalent circuit model of the MHD angular velocity sensor based on the shape data of the upper electrode, the lower electrode, the conductive fluid ring, the magnetic core, the primary winding and the secondary winding in the MHD angular velocity sensor, wherein the shape data comprises size data, material data and shape data; determining the noise voltage power spectrum density of the MHD angular velocity sensor based on the parameter values ​​of the multiple equivalent components and the turns ratio of the primary winding to the secondary winding; determining the background noise of the MHD angular velocity sensor based on the noise voltage power spectrum density of the MHD angular velocity sensor.

The invention discloses a target feature agility system and method for target stealth, and the system comprises a signal processing subsystem which is used for sorting and recognizing signals, and deploying a complete RCS database and agility style of a target body; the intermediate frequency processing subsystem is used for collecting and analyzing intermediate frequency data, generating agile waveforms and outputting the intermediate frequency data; the radio frequency processing and transmitting subsystem is used for receiving the multiple paths of agile intermediate frequency signals, converting the multiple paths of agile intermediate frequency signals into multiple paths of radio frequency signals through frequency mixing, and outputting the multiple paths of radio frequency signals through power amplification; the radio frequency processing and receiving subsystem is used for mixing the radio frequency signals and then converting the radio frequency signals into intermediate frequency signals; and the air feed system is used for radiating the received signal to the space according to a specific polarization mode and receiving an enemy radar waveform at the same time. According to the method, the ground noise of the echo signal can be enhanced, the signal-to-noise ratio of the echo signal of the target body is weakened, the radar CFAR detection threshold is improved, and the radar detection distance is shortened; the agility mode is flexible and variable, the adaptive scene is wide, the engineering implementation is high, and an effective stealth means is provided.

The invention relates to an atmospheric composition detector which is characterized in that under the excitation of detection gas molecules in an ionization inner space by an ultraviolet light generator in a gas sensor module, pollutant detection gas molecules in the ionization inner space are ionized into charged particles, and the charged particles can be detected by a signal detection part; under the action of the air pump, air can continuously enter the gas sensor module to be detected, the stable state of the air pressure of the air entering the gas sensor module is guaranteed, the air entering the gas channel of the sensor module can be discharged only after passing through the ionization inner space, more air participates in the ionization process, and the ionization efficiency is improved. Compared with the prior art, the method has the advantages that the scale of charged particles is increased, the semaphore is larger, and the signal-to-noise ratio is better under the condition that the ground noise is kept unchanged, so that more effective detection sensitivity is provided, the method better conforms to the external air concentration, stable air pressure ensures that ionized signals cannot deviate from the actual pollutant concentration condition due to air pressure fluctuation, and the detection quality is higher.